Lack of mask use during large public events might spread COVID-19. It is now possible to measure this and similar public health information using publicly available webcams. We demonstrate a rapid assessment approach for measuring mask usage at a public event.

We monitored crowds at public areas in Sturgis, SD using a live, high-definition, town-sponsored video stream to analyze the prevalence of mask wearing. We developed a rapid coding procedure for mask wearing and analyzed brief (5 to 25 min) video segments to assess mask-wearing compliance in outdoor public areas. We calculated compliance estimates and compared reliability among the human coders.

We were able to observe and quantify public behavior on the public streets. This approach rapidly estimated public health information (e.g., 512 people observed over 25 minutes with 2.3% mask usage) available on the same day. Coders produced reliable estimates across a sample of videos for counting masked users and mask-wearing proportion. Our video data is stored in Databrary.org.

This approach has implications for disaster responses and public health. The approach is easy to use, can provide same day results, and can provide public health stakeholders with key information on public behavior.